Summary of Holistic Automated Red Teaming For Large Language Models Through Top-down Test Case Generation and Multi-turn Interaction, by Jinchuan Zhang et al.
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction
by Jinchuan Zhang, Yan Zhou, Yaxin Liu, Ziming Li, Songlin Hu
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach, HARM (Holistic Automated Red teaMing), is proposed to improve automated red teaming for large language models (LLMs). The existing methods focus on improving attack success rates but neglect comprehensive test case coverage. HARM scales up diversity using a top-down risk taxonomy and fine-tuning strategy, enabling multi-turn adversarial probing like humans. Experimental results show that HARM provides a systematic understanding of model vulnerabilities and targeted guidance for the alignment process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated red teaming helps identify misaligned behaviors in large language models (LLMs). Existing methods focus on improving attacks, but overlook test case coverage. A new method, HARM, is proposed to overcome these limitations. It uses a risk taxonomy and fine-tuning strategy to create diverse test cases and simulate human-like interactions. This makes it easier to understand model weaknesses and improve how well models work with humans. |
Keywords
» Artificial intelligence » Alignment » Fine tuning
